Formation Control and Trajectory Tracking of Nonholonomic Mobile Robots

Saradagi, Akshit; Muralidharan, Vijay; Krishnan, Vishaal; Menta, Sandeep; Mahindrakar, Arun D.

doi:10.1109/tcst.2017.2749563

Cited by 46 publications

(26 citation statements)

References 17 publications

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“…As discussed in Section 1, the main aim of this paper is based on the concept of SMC for single robot under polar coordinate in References 17,18, to develop a SMC methodology for cooperative distributed consensus tracking for the network of MNWMRs from the view of multi-agent systems. By using the control input (25), it is easy to observe that the desired state zero is not an intrinsic equilibrium point of the closed-loop continuous controlled system (26) due to the nonzero input disturbance f i . The proposed criterion (27) in Theorem 1 can fully guarantee that the asymptotic consensus-tracking errors are significantly small in both position and heading-direction when the designed control gains ||Q i || are chosen to be large enough.…”

Section: Theoremmentioning

confidence: 99%

“…22 Numerous consensus or formation protocols (or algorithms) have been proposed for MASs from various perspectives, with the analysis performed in the framework of Cartesian coordinates or polar coordinates. [23][24][25][26] In contrast to the Cartesian coordinates, the polar coordinate representation of NWMR is more often adopted to describe the collective rotating formation problem for a group of multi-agent dynamic systems, originating from many potential applications such as formation flight of satellites around the earth, circular mobile sensor networks, and spacecraft docking. 27,28 Due to the nonholonomic constraints, the design of cooperative tracking scheme for MNWMRs becomes more challenging than their single-robot counterparts in particular when the communication interactions among the networked robots are taken into account with an increase of group size.…”

Section: Introductionmentioning

confidence: 99%

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Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Liu

Guo

et al. 2020

Intl J Robust & Nonlinear

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This paper proposes a sliding-mode control (SMC) method to achieve practical cooperative consensus tracking for a network of multiple nonholonomic wheeled mobile robots (MNWMRs) with input disturbances. A novel SMC surface under the nonholonomic constraints is first formulated to characterize the network communication interactions among the networked robots under the framework of polar coordinates. A unified distributed consensus tracking strategy is then proposed by systematically combining a position controller and a direction controller. Furthermore, a simple yet general criterion is derived to achieve the desired practical consensus of trajectory tracking and posture stabilization for MNWMRs. In particular, for a specific common consensus trajectory, the complete asymptotic tracking in heading direction can be fully guaranteed when the perfect asymptotic position-tracking errors are realized. Accordingly, the developed consensus tracking strategy for MNWMRs demonstrates some advantages of control performance including stability, robustness, and effectiveness over the existing control method proposed for their single-robot counterparts. Some comparative simulation results are given to confirm the effectiveness of the proposed cooperative consensus control method. K E Y W O R D Smulti-nonholonomic mobile robots, polar coordinates, practical consensus tracking, sliding-mode control 1 Int J Robust Nonlinear Control. 2020;30:3831-3847.wileyonlinelibrary.com/journal/rnc

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Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Liu

Guo

et al. 2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

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“…Let e o = [e 1 e 2 ] T be an estimate error with e 1 = z 1 − x 1 and e 2 = z 2 − x 2 , respectively. The ESO for systems (10) and (11) is designed as follows:…”

Section: Design Of Esomentioning

confidence: 99%

“…where k 1 is a adjustable gain, and z 2 ∕b 0 is a compensation for systems (10) and (11). Note that the total gains of the dynamic controller for systems (8) and (9) are labeled with k = diag{k 1 k 2 }.…”

Section: Design Of Nonlinear State Error Feedback Controllermentioning

confidence: 99%

“…Song et al studied a tracking control problem of multiple‐input–multiple‐output nonaffine systems based on the readily computable deep‐rooted information. In the work of Saradagi et al, multitime scale implementation methods were used to achieve multiobjective tasks for a nonholonomic mobile inverted pendulum robot under a two‐loop control architecture. Through the aforementioned analysis, a dual closed‐loop control structure is considered to address the problem of both tracking control and the external disturbances for the WMR.…”

Section: Introductionmentioning

confidence: 99%

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Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Yang

Guo

Xia

et al. 2019

Intl J Robust & Nonlinear

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Summary A dual closed‐loop tracking control is proposed for a wheeled mobile robot based on active disturbance rejection control (ADRC) and model predictive control (MPC). In the inner loop system, the ADRC scheme with an extended state observer (ESO) is proposed to estimate and compensate external disturbances. In the outer loop system, the MPC strategy is developed to generate a desired velocity for the inner loop dynamic system subject to a diamond‐shaped input constraint. Both effectiveness and stability analysis are given for the ESO and the dual closed‐loop system, respectively. Simulation results demonstrate the performances of the proposed control scheme.

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Adaptive fuzzy finite‐time formation asymptotic tracking control for nonholonomic multirobot systems with inputs quantization

Dong,

2023

Adaptive Control & Signal

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SummaryThis paper investigates the finite‐time formation tracking control problem for nonholonomic multirobot systems with inputs quantization. The fuzzy logic systems (FLSs) and adaptive method are introduced to approximate unknown nonlinear functions and parameters. Multirobot needs to keep safe and effective communicating range, the barrier function and prescribed performance method are employed to ensure collision avoidance and connectivity maintenance. Then, in order to make each robot more precisely and quickly track referenced leader's trajectory and consider the finite channel bandwidth in practical applications, the travel range of robots can be maintained at more secure region and formation can operate normally within the given bandwidth. By combining the command filter and finite‐time theory, a novel decentralized finite‐time formation asymptotic tracking (FTFAT) control strategy is further proposed based on inputs quantization technology, it guarantees that all signals are bounded and the formation tracking errors asymptotically converge to zero in finite‐time. Finally, the practicability of FTFAT strategy is demonstrated absolutely for nonholonomic multirobot systems.

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Formation Control and Trajectory Tracking of Nonholonomic Mobile Robots

Cited by 46 publications

References 17 publications

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Adaptive fuzzy finite‐time formation asymptotic tracking control for nonholonomic multirobot systems with inputs quantization

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